Scale-invariant analysis of wear particle surface morphology: III: Pattern recognition

Abstract

A new scale-invariant pattern recognition method has been developed for the characterization of wear particle surface morphology. The method is based on a partition-iterated function system (PIFS) constructed for a wear particle image. A database containing PIFSs constructed for different classes of wear particles was built. Each class is a set of wear particles exhibiting a similar morphology. The objective of pattern recognition is to assign a new wear particle, which is not yet classified, into one of these particle classes. For the classification, PIFSs taken from the database are decoded with the unclassified particle image used as an initial image. Decoded images obtained from PIFSs are then individually compared with the new particle image. If the difference calculated between these images is less than a threshold value, the new particle is assigned to a class. Problems associated with the use of the pattern recognition method, such as rotational- and translation-invariance, comparison between two images and computer implementation, are addressed in this paper. The effects of image resolution, tilt angle, noise, gain variations and defocusing were also investigated. Results obtained for computer-generated images of fractal surfaces and microscopic images of wear particles are demonstrated.